• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.2
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• pp.125-138
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• 2016

Currently, there is increasing demand for weather information, however, providing meteorology and climate information is limited. In order to improve them, supporting the meteorology and climate big data platform use and training the meteorology and climate big data specialist who meet the needs of government, public agencies and corporate, are required. Meteorology and climate big data requires high-value usable service in variety fields, and it should be provided personalized service of industry-specific type for the service extension and new content development. To provide personalized service, it is essential to build the collaboration ecosystem at the national level. Building the collaboration ecosystem environment, convergence of marine policy and climate policy, convergence of oceanography and meteorology and convergence of R&D basic research and applied research are required. Since then, demand analysis, production sharing information, unification are able to build the collaboration ecosystem.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.447-454
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• 2013

As the surveillance CCTVs are increasing every year, big data image processing for the CCTV image data has become a hot issue. In this paper, we propose a Hadoop-based big data image processing technique to recognize a vehicle number from a large amount of automatic number plate images taken from CCTVs. We also implement the vehicle route tracking system that displays the moving path of the searched vehicle on Google Maps with the related information together. In order to evaluate the performance we compare and analysis the vehicle number recognition time for a lot of CCTV image data in Hadoop and the single PC environment.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1575-1584
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• 2017

As interest in big data has increased recently, NoSQL, a solution for storing and processing big data, is getting attention. NoSQL supports high speed, high availability, and high scalability, but is limited in areas where data integrity is important because it does not support multiple row transactions. To overcome these drawbacks, many studies are underway to support multiple row transactions in NoSQL. However, existing studies have a disadvantage that the number of transactions that can be processed per unit of time is low and performance is degraded. Therefore, in this paper, we design and implement a multi-row transaction system for data integrity in big data environment based on HBase, a column-based NoSQL which is widely used recently. The multi-row transaction system efficiently performs multi-row transactions by adding columns to manage transaction information for every user table. In addition, it controls the execution, collision, and recovery of multiple row transactions through the transaction manager, and it communicates with HBase through the communication manager so that it can exchange information necessary for multiple row transactions. Finally, we performed a comparative performance evaluation with HAcid and Haeinsa, and verified the superiority of the multirow transaction system developed in this paper.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.269-276
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• 2015

Recently, with the activation of the industry related to the big data, the global security companies have expanded their scopes from structured to unstructured data for the intelligent security threat monitoring and prevention, and they show the trend to utilize the technique of user's tendency analysis for security prevention. This is because the information scope that can be deducted from the existing structured data(Quantify existing available data) analysis is limited. This study is to utilize the analysis of security tendency(Items classified purpose distinction, positive, negative judgment, key analysis of keyword relevance) applying the machine learning algorithm($Na{\ddot{i}}ve$ Bayes, Decision Tree, K-nearest neighbor, Apriori) in the big data environment. Upon the capability analysis, it was confirmed that the security items and specific indexes for the decision of security tendency could be extracted from structured and unstructured data.

• Korean Management Science Review
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• v.12 no.1
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• pp.125-138
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• 1995

This paper is to develop an integrated environment software on MS-DOS for linear programming. For the purpose, First, the linear programming integrated environment software satisfying both the educational purpose and the professional purpose was designed and constructed on MS-DOS. Second, the text editor with big capacity was developed. The arithmetic form analyser was also developed and connected to the test editor so that users can input data in the arithmetic form. As a result, users can learn and perform linear programming in the linear programming integrated environment software.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.615-622
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• 2014

The Korean film market has rapidly achieved an international scale, and this has led to a need for decision-making based on analytical methods that are more precise and appropriate. In this modern era, a highly advanced information environment can provide an overwhelming amount of data that is generated in real time, and this data must be properly handled and analyzed in order to extract useful information. In particular, the preprocessing of large data, which is the most time-consuming step, should be done in a reasonable amount of time. In this paper, we investigated a big data preprocessing method for predicting movie box office success. We analyzed the movie data characteristics for specialized preprocessing methods, and used the Hadoop MapReduce framework. The experimental results showed that the preprocessing methods using big data techniques are more effective than existing methods.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.684-690
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• 2018

In society of the big data is being recognized as one of the core technologies witch is analysis of the collected information, the intelligent evolution of society seems to be more oriented society through an optimized value creation based on a prediction technique. If we take advantage of technologies based on big data about various data and a large amount of data generated during system operation, it will be possible to support stable operation and prevention of faults and failures. In this paper, we suggested an environment using the collection and analysis of big data, and proposed an derive time series prediction model for predicting failure through server performance monitoring for data collected and analyzed. It can be capable of supporting stable operation of the IT systems through failure prediction model for the server operator.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.55-58
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• 2024

In this paper, we propose a design and implementation of big data-based fine dust anomaly detection machine learning system. The proposed is system that classifies the fine dust air quality index through meteorological information composed of fine dust and big data. This system classifies fine dust through the design of an anomaly detection algorithm according to the outliers for each air quality index classification categories based on machine learning. Depth data of the image collected from the camera collects images according to the level of fine dust, and then creates a fine dust visibility mask. And, with a learning-based fingerprinting technique through a mono depth estimation algorithm, the fine dust level is derived by inferring the visibility distance of fine dust collected from the monoscope camera. For experimentation and analysis of this method, after creating learning data by matching the fine dust level data and CCTV image data by region and time, a model is created and tested in a real environment.

• Journal of the Ergonomics Society of Korea
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• v.34 no.2
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• pp.135-143
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• 2015

Objective: The purposes of this study are to investigate ergonomic problems and to propose their improving measures in office environment of big general hospitals. Background: Office tasks have varying risk factors of work-related musculoskeletal disorders (WMSDs). The first symptom resulted from the office work was recognized as an occupational disease in Korea in 1986. Although the symptoms have increased since its first recognition, there has been few study on the effect of office work environment settings. Method: First, the author took pictures of working scenes performed in three big university hospitals. Next, the pictures were analyzed in view point of ergonomics. Based on the analysis, their improving measures were proposed for reducing work stress. Results: The results showed that most physical office environment settings such as dimensions of tables/desks and chairs, leg room, thigh, knee and foot clearances, and chairs used did not satisfy the ergonomically recommended design guidelines. In addition, some clerks placed personal belongings under their desks, put monitors in high position and did not lean against the backrest of chairs in seated tasks, which resulted in poor working postures of leg, back, neck etc. It is recommended that the hospital management should provide their clerks with ergonomically designed office furniture and continuously perform ergonomics training program for raising clerks' recognition for office ergonomics. Conclusion: Most office environment settings investigated in this study were not in good condition in view point of ergonomic design for the settings. Application: It would be useful as basic data for establishing ergonomically good office environment in hospitals.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.289-295
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• 2021

Today, the most important factor affecting the quality of human life is thought to be due to the environment. The importance of environmental monitoring systems to improve human life and improve welfare as the magnitude of the damage increases year by year due to the rapid increase in the frequency of hail, typhoons, collapse of incisions, landslides, etc. Is increasing day by day. Among environmental problems, problems caused by water quality have a very high proportion, and as there is a growing concern that the scale of damage will increase when water pollution accidents occur due to urbanization and industrialization, the demand for social water safety nets is increasing. have. In the last 5 years, 259 cases of water pollution (Han River 99, Nakdong River 31, Geum River 25, Seomjin River and Yeongsan River 19, and 85 others) have occurred in the four major river basins. Caused damage. Therefore, it is required to establish a water quality environment management strategy system based on big data that can minimize the uncertainty of the water quality environment by expanding the target of water quality management from the current water quality management system centered on the four major rivers to small and medium-sized rivers, tributaries/branches, and reservoirs. In this paper, we intend to construct and analyze a water quality monitoring system based on big data that can present useful water quality environment information by analyzing the water quality information accumulated for a long time.